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The inclusion of seasonal dummy variables to a multiple regression model may help eliminate autocorrelation if the data are characterized by seasonal fluctuations. A. perfect multicollinearity. B. bias in OLS slope estimates caused by autocorrelation. C. near multicollinearity. D. All of the options are correct.

Sagot :

The correct option among the given is option B , bias in OLS slope estimates caused by autocorrelation.

In the presence of autocorrelation, as in the case of heteroscedasticity, the OLS estimators are still linearly unbiased, consistent, and asymptotically normally distributed, but they are no longer efficient (i.e., minimum variance).

A dummy variable is a binary variable with a value of either 0 or 1. Such variables are added to a regression model to represent binary factors, which are either observed or not observed.

A dummy variable can be used to indicate whether a data point possesses a specific property. A dummy variable, for example, can be used to indicate whether a car engine is 'Standard' or 'Turbo.' Or whether a participant in a drug trial is in the placebo or treatment groups.

To learn more about autocorrelation

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